Point: Fluoroquinolone-Based Antibacterial Chemoprophylaxis in Neutropenic Cancer Patients Works for Defined Outcomes in Defined Populations, but Must Be Used Wisely
Bibliographic record
Abstract
Fluoroquinolone-based antibacterial chemoprophylaxis administered in situations in which the prevalence of fluoroquinolone-resistant Escherichia coli is low (< 3% to 5%) can reliably reduce the risk for invasive gram-negative bacillary infection, and, if supplemented by gram-positive agents such as rifampin, penicillin, or macrolides, can reduce the risk of developing invasive infections caused by gram-positive microorganisms, including Viridans streptococci and coagulase-negative staphylococci. In the published literature, fluoroquinolone-based chemoprophylaxis does not reliably reduce the incidence of febrile neutropenic episodes, neutropenic episode-related mortality, or physician-initiated systemic antimicrobial prescribing behavior. Prophylaxis should only be prescribed in defined patient populations from the first day of cytotoxic therapy until neutrophil regeneration in environments in which the prevalence of gram-negative bacillary resistance to the prophylaxis strategy is low. Small phase II clinical trials suggest that empirical antibacterial therapy of unexplained fevers in neutropenic patients receiving effective fluoroquinolone-based prophylaxis under defined epidemiologic circumstances may be safely discontinued early. Better discriminators of infection in febrile neutropenic patients are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".